Bibliographic record
Abstract
Book reviewed in this article: Résumé Ce livre est un appel à la réflexion sur la gestion prévisionnelle et stratégique des ressources humaines dans l'entreprise tunisienne. Son défi est double: aller au delà de la dimension administrative de la GRH en jetant les bases d'une gestion prévisionnelle et stratégique, et dépasser les approches universalistes du management en traitant des spécificités de l'entreprise tunisienne. Le texte analyse les préalables nécessaires à la mise en place d'une gestion prévisionnelle et stratégique des personnes. Les chapitres couvrent toutes les pratiques de GRH associées à cette finalité. Le lecteur appréciera également les chapitres sur la globalisation et les ressources humaines et sur la dimension stratégique des ressources humaines. Cet ouvrage l'aidera certainement à mieux comprendre le positionnement stratégique de la GRH. Abstract This book is intended to stimulate thought on strategic human resources management (SHRM). Its challenge is twofold. First, it aims at calling into question the administrative approaches to HRM by laying the foundations for employment planning and SHRM. Second, it highlights the specificities of the Tunisian organizations and urges scholars to move beyond the universalistic approaches to HRM. The book deals with the prerequisites for employment planning and reviews all the HRM practices that such a move will entail. It also focuses on globalization and competition within the HRM field. This book will certainly help broaden the reader's understating of the new strategic position of HRM.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.614 | 0.554 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".